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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.9212222639305973

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.9094896980313647

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.8903518101434769

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.8988457207207208

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.9226903987320654

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.9193641558224892

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Random Forest Accuracy = 0.9156792208875543

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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels

0 0 0 0 ... 0 0 0

1 0 0 0 ... 0 0 0

2 0 0 0 ... 0 0 0

3 0 0 0 ... 0 0 0

4 0 0 0 ... 0 0 0


[5 rows x 44 columns]

Traceback (most recent call last):


File C:\Dev\Python\biomedical_term\Lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec

exec(code, globals, locals)


File c:\dev\python\biomedical_term\homew.py:453

result_selected_perm=model_perm.predict(X_perm)


File C:\Dev\Python\biomedical_term\Lib\site-packages\sklearn\ensemble\_forest.py:820 in predict

proba = self.predict_proba(X)


File C:\Dev\Python\biomedical_term\Lib\site-packages\sklearn\ensemble\_forest.py:873 in predict_proba

Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(


File C:\Dev\Python\biomedical_term\Lib\site-packages\sklearn\utils\parallel.py:63 in __call__

return super().__call__(iterable_with_config)


File C:\Dev\Python\biomedical_term\Lib\site-packages\joblib\parallel.py:1098 in __call__

self.retrieve()


File C:\Dev\Python\biomedical_term\Lib\site-packages\joblib\parallel.py:975 in retrieve

self._output.extend(job.get(timeout=self.timeout))


File ~\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\pool.py:768 in get

self.wait(timeout)


File ~\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\pool.py:765 in wait

self._event.wait(timeout)


File ~\AppData\Local\Programs\Python\Python311\Lib\threading.py:622 in wait

signaled = self._cond.wait(timeout)


File ~\AppData\Local\Programs\Python\Python311\Lib\threading.py:320 in wait

waiter.acquire()


KeyboardInterrupt


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In [9]: